期刊名称：Signal & Image Processing : An International Journal (SIPIJ)
印刷版ISSN：2229-3922
电子版ISSN：0976-710X
出版年度：2013
卷号：4
期号：4
页码：31
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Vehicle detection in traffic scenes is an important issue in driver assistance systems and self-guidedvehicles that includes two stages of Hypothesis Generation (HG) and Hypothesis Verification (HV). Theboth stages are important and challenging. In the first stage, potential vehicles are hypothesized and in thesecond stage, all hypotheses are verified and classified into vehicle and non-vehicle classes. In this paper,we present a method for detecting front and rear on-road vehicles without lane information and priorknowledge about the position of the road. In the HG stage, a three-step method including shadow, textureand symmetry clues is applied. In the HV stage, we extract Pyramid Histograms of Oriented Gradients(PHOG) features from a traffic image as basic features to detect vehicles. Principle Component Analysis(PCA) is applied to these PHOG feature vectors as a dimension reduction tool to obtain the PHOG-PCAvectors. Then, we use Genetic Algorithm (GA) and linear Support Vector Machine (SVM) to improve theperformance and generalization of the PHOG-PCA features. Experimental results of the proposed HVstage showed good classification accuracy of more than 97% correct classification on realistic on-roadvehicle dataset images and also it has better classification accuracy in comparison with other approaches.
